Strategies and Heuristics in the Analysis of a Natural Language in Machine Translation

STRATEGIES AND HEURISTICS iN THE ANALYSIS OF A NATURAL LANGUAGE IN MACHINE TRANSLATION (In the memory of Bernard Vauquois) Zaharin Yusoff Groupe d'Etudes pour la Traduction Automatique BP n ° 68 Universit6 de Grenoble 38402 Saint-Martin-d'H@res FRANCE ABSTRACT The analysis phase in an indirect, transfer and global approach to machine translation is studied. The analysis conducted can be described as exhaus- tive (meaning with backtracking), depth-first and strategically and heuristically driven, while the gran~nar used is an augmented context free grammar. The problem areas, being pattern matching, ambigui- ties, forward propagation, checking for correctness and backtracking, are highlighted. Established results found in the literature are employed whenever adaptable, while suggestions are given otherwise. I. INTRODUCTION We interest ourselves in the analysis phase of a machine translation system which adopts the indirect, transfer and global approach (see [Slocum 84]). The aim of this paper is to clarify the problem areas, suggest a few solutions, and point out the loose ends. There is no current implementation for the analyser we describe, and the ideas are basically a reflection of ~hat ~c would like to see in an MT system, based on previous experience in the field. A very important issue is to separate the linguistic knowledge (the grammar) from the algorithmic and technical knowledge (the organ]sat]on of the analyser, pattern matching, etc.). "Approximate" linguistic knowledge is also separated and used as a means to guide the analysis rather than considered as absolute (semantics and context constraints as heuristics instead of grammar rules). Due to space restrictions, we shall immedia- tely specify the basic type of analyser we shall be working with, without giving any reasons for the choice. The interested reader is referred to [Zaharin 85] for an uncondensed version of this paper, and [Zaharin 86] for more details. 2. THE ANALYSER In general, an analyser can be viewed as a black box with two holes, where we insert the input text through one and it gives the output linguistic structure through the other (in our case, an annotated tree representing the "meaning" of the input text). Peeping into the box, we would notice that it works in cycles doing the following five steps until it triggers off some stopping mechanism and hence furnishing the output : a) computing the object set ; b) choosing an object ; c) computing the rule set ; d) choosing a rule ; e) applying the chosen rule on the chosen object. Naturally, depending on the various models, these steps need not be executed in the given order, nor are they necessarily as clear out. Indeed, some may even execute the cycles in parallel. Our analyser will do the five steps in the following manner. Steps (a) and (c) will be done together, computing all objects on which some rule is applicable, and to each of these objects, the set of all applicable rules is computed. The result is a set of ].inked pairs O-R where R is a rule applicable on the object O. A …

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